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RootDriver

根源出发,驱动万物

一个轻量级的 Python AI Agent 开发框架。

解决痛点:

  • 想快速跑一个 Agent?一行代码,不需要编排、不需要懂设计模式
  • 市面框架太重、太复杂、学习成本高?RootDriver 核心代码几千行,不用记几百个 API
  • 需要扩展?按需添加,想加什么加什么,不被框架绑架

特性

  • 简洁易用:装饰器方式定义工具,快速构建 Agent
  • 模块化设计:LLM 适配器、工具系统、会话管理解耦
  • 工具调用:支持 function calling,自动执行工具并返回结果
  • 状态管理:支持内存和数据库持久化检查点
  • 异常体系:完整的异常类层次结构
  • 异步支持:全面异步支持,并发 Agent / 工具调用

安装

pip install rootdriver

快速开始

定义工具

from rootdriver import tool

@tool
def get_weather(city: str) -> str:
    """获取城市天气"""
    return f"{city} 晴天"

创建 Agent

from rootdriver import Agent, LLMConfig, OpenAIAdapter

llm_config = LLMConfig(
    model="gpt-4",
    adapter=OpenAIAdapter(
        api_key="YOUR_API_KEY",
        base_url="BASE_URL"
    )
)

agent = Agent.create(
    llm_config=llm_config,
    tools=[get_weather],
    system_prompt="你是一个有用的助手",
)

# 单次对话(无 tool)
response = agent.talk("北京天气怎么样?")
print(response)

使用工具

# 完整对话循环(包含工具调用)
response = agent.react("帮我查下上海天气")
print(response)

异步用法

import asyncio
from rootdriver import Agent, LLMConfig, OpenAIAdapter

async def main():

    # 单次异步对话
    response = await agent.atalk("你好")
    print(response)

    # 并发多个 Agent
    results = await asyncio.gather(
        agent.areact("问题1"),
        agent.areact("问题2"),
        agent.areact("问题3"),
    )

asyncio.run(main())

记忆持久化

Agent 支持对话历史持久化到 JSON 文件:

agent = Agent.create(
    llm_config=llm_config,
    system_prompt="你是一个有用的助手",
    db_path="conversations.json",
)

# 对话
response = agent.react("我们之前聊了什么?")

# 手动保存到数据库
agent.conversation_repo.db_opt.update(
    agent.engine.conversation.get_messages(),
    checkpoint_name="session_1"
)

# 从数据库恢复对话历史
messages = agent.conversation_repo.db_opt.get("session_1")
agent.engine.conversation.update_message(messages)

内存快照

# 保存到内存快照
agent.conversation_repo.buffer_opt.update(
    agent.engine.conversation.get_messages(),
    checkpoint_name="backup_point"
)

# 从内存快照恢复
messages = agent.conversation_repo.buffer_opt.get("backup_point")

核心组件

组件 说明
Agent 智能体入口,整合 LLM、工具、会话
Engine 核心引擎,处理对话循环和工具调用
Conversation 会话管理,维护消息历史
LLM LLM 调用封装
Tool 工具集合,管理所有可调用工具
State 状态管理,支持检查点和持久化
JsonDB JSON 文件数据库封装

项目结构

rootdriver/
├── __init__.py        # 包入口,导出核心组件和异常
├── agent.py           # Agent 智能体
├── engine.py          # 引擎核心(DBOpt/BufferOpt/ConversationRepo)
├── conversation.py    # 对话管理
├── conversation_repo.py # 会话持久化仓库
├── exceptions.py      # 异常定义
├── constants.py       # 常量定义
├── db/                # 数据库封装包
│   ├── __init__.py
│   ├── base_db.py     # 数据库抽象基类
│   └── json_db.py     # JsonDB 实现
├── llm/
│   ├── __init__.py
│   ├── llm.py         # LLM 封装
│   ├── base_adapter.py    # 适配器基类
│   └── adapter/
│       ├── __init__.py
│       ├── anthropic_adapter.py  # Anthropic 适配器
│       └── openai_adapter.py     # OpenAI 兼容适配器
├── tool/
│   ├── __init__.py
│   ├── base_tool.py   # 工具基类
│   └── tools.py       # 工具集
├── types/             # 类型定义
│   ├── __init__.py
│   ├── config.py      # LLMConfig 等配置类型
│   ├── message.py     # Message 消息类型
│   ├── llm.py         # LLMRequest/LLMResponse
│   └── tool.py        # ToolDefinition/ToolCall
└── utils/             # 工具函数
    ├── __init__.py
    ├── build_message.py   # 消息构建函数
    ├── file.py        # 文件操作
    ├── optional_dependence.py  # 可选依赖检测
    ├── strip_think.py # 去除思考过程标签
    └── time.py        # 时间工具

License

MIT

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